The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 183–187, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-183-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 183–187, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-183-2020

  06 Nov 2020

06 Nov 2020

APPLICATION OF SEMANTIC SEGMENTATION WITH FEW LABELS IN THE DETECTION OF WATER BODIES FROM PERUSAT-1 SATELLITE’S IMAGES

J. Gonzalez1, K. Sankaran2, V. Ayma1, and C. Beltran1 J. Gonzalez et al.
  • 1Pontifical Catholic University of Peru, Lima, Peru
  • 2Mila, Université de Montréal, Montreal, Canada

Keywords: Semantic segmentation, remote sensing, water bodies detection, satellite images, PeruSAT-1

Abstract. Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.

In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved.